Method and System for Managing Crowd-Sensed Data

1. A method for managing crowd-sensed data, associated with events occurring in a geographical area, at a computing device, said method comprising: receiving, by one or more transceivers at the computing device, the crowd-sensed data from one or more data sources, wherein the crowd-sensed data comprises one or more event reports associated with at least a type of each of one or more events reported by the one or more data sources and a particular geographical area associated with the one or more events, and wherein the crowd-sensed data is received in real-time; generating, by one or more processors at the computing device, a data structure based on an aggregation of the received crowd-sensed data; determining, by the one or more processors at the computing device, first information and second information based on at least the generated data structure, a reputation score associated with each of the one or more data sources and metadata associated with each of the one or more event reports, wherein the first information corresponds to a likelihood of an occurrence of one or more other events at the particular geographical area, and wherein the first information is determined using: Z ⁡ ( j ) = A ⁡ ( j ) × d j ′ A ⁡ ( j ) × d j ′ + B ⁡ ( j ) × ( 1 - d j ) ′ ; and displaying, by the one or more processors at the computing device, at least the determined first information and the determined second information through a user interface displayed on a display screen of a requestor-computing device based on at least a prioritization of the one or more events, wherein the one or more other events occur as a direct result of the occurrence of the one or more events, wherein the one or more other events correspond to traffic-related events, and wherein the traffic-related events include one or more of traffic jams, vehicle detours, and road accidents.

2. The method of claim 1 , wherein the crowd-sensed data corresponds to one or more electronic messages posted, shared, liked, or disliked by one or more users, associated with the one or more data sources, pertaining to each of the one or more events.

3. The method of claim 1 , wherein the crowd-sensed data further includes at least an occurrence time stamp associated with each of the one or more event reports and the geographical area associated with each of the one or more event reports.

4. The method of claim 1 , wherein the received crowd-sensed data is aggregated based on at least a reporting time stamp and the geographical area associated with each of the one or more event reports in the crowd-sensed data.

5. The method of claim 1 , wherein a count of rows in the generated data structure is determined based on the one or more data sources, and wherein a count of columns in the generated data structure is determined based on the type of each of the one or more events.

6. The method of claim 5 , further comprising determining, by the one or more processors, a score pertaining to each cell, that corresponds to a specific data source represented by a row and a specific event represented by a column, in the generated data structure based on the reputation score of the specific data source and a number of times the specific event in each of the one or more events is reported by one or more users on the one or more data sources.

7. The method of claim 6 , wherein the reputation score of a data source is determined based on at least one or more event reports in the data source, a veracity associated with each of the one or more event reports, a reporting time stamp associated with each of the one or more event reports, and a criticality score associated with each of the one or more events.

8. The method of claim 7 , wherein the veracity associated with each of the one or more event reports is based on the first information and the second information.

9. The method of claim 7 , wherein the criticality score of each of the one or more events is based on at least historical data associated with each of the one or more events.

10. The method of claim 1 , wherein the first information is determined based on one or more preferences of a requestor for each of the one or more events.

11. The method of claim 1 , wherein the determined second information corresponds to a data source reliability score that is determined based on a reporting time stamp associated with each of the one or more event reports, a criticality score associated with each of the one or more event reports, a pre-determined category of the one or more data sources, and/or a likelihood of occurrence of one or more other events due to the occurrence of each of the one or more events.

12. The method of claim 1 , wherein the metadata associated with each of the one or more event reports comprises an occurrence time stamp associated with each of the one or more event reports, a geographical location associated with each of the one or more event reports, and a criticality score associated with each of the one or more events.

13. The method of claim 1 , wherein the aggregation is based on at least the one or more data sources and the type of each of the one or more events associated with the crowd-sensed data.

14. The method of claim 1 , further comprising displaying, by the one or more processors, the reputation score and a likelihood of occurrence of one or more other events as result of the occurrence of each of the one or more events on the display screen of the computing device of a requestor.

15. The method of claim 1 , wherein the prioritization of each of the one or more events is determined based on a criticality score of each of the one or more events.

16. A system for managing crowd-sensed data, associated with events occurring in a geographical area, at a computing device, said system comprising: one or more transceivers at the computing device configured to: receive the crowd-sensed data from one or more data sources, wherein the crowd-sensed data comprises one or more event reports associated with at least a type of each of one or more events reported by the one or more data sources and a particular geographical area associated with the one or more events, and wherein the crowd-sensed data is received in real-time; one or more processors at the computing device configured to: generate a data structure based on an aggregation of the received crowd-sensed data; determine first information and second information based on at least the generated data structure, a reputation score associated with each of the one or more data sources and metadata associated with each of the one or more event reports, wherein the first information corresponds to a likelihood of an occurrence of one or more other events at the particular geographical area, and wherein the first information is determined using: Z ⁡ ( j ) = A ⁡ ( j ) × d j ′ A ⁡ ( j ) × d j ′ + B ⁡ ( j ) × ( 1 - d j ) ′ ; and display at least the determined first information and the determined second information through a user interface displayed on a display screen of a requestor-computing device based on at least a prioritization of the one or more events, wherein the one or more other events occur as a direct result of the occurrence of the one or more events, wherein the one or more other events correspond to traffic-related events, and wherein the traffic-related events include one or more of traffic jams, vehicle detours, and road accidents.

17. The system of claim 16 , wherein the crowd-sensed data corresponds to one or more electronic messages posted, shared, liked, or disliked by one or more users, associated with the one or more data sources, pertaining to each of the one or more events.

18. The system of claim 16 , wherein the crowd-sensed data further includes at least an occurrence time stamp associated with each of the one or more event reports and the geographical area associated with each of the one or more event reports.

19. The system of claim 16 , wherein said one or more processors are further configured to aggregate the received crowd-sensed data based on at least a reporting time stamp and the geographical area associated with each of the one or more event reports in the crowd-sensed data.

20. The system of claim 16 , wherein a count of rows in the generated data structure is determined based on the one or more data sources and wherein a count of columns in the generated data structure is determined based on the type of each of the one or more events.

21. The system of claim 20 further comprising determining, by the one or more processors, a score pertaining to each cell, that corresponds to a specific data source represented by a row and a specific event represented by a column, in the generated data structure based on the reputation score of the specific data source and a number of times the specific event in each of the one or more events is reported by one or more users on the one or more data sources.

22. The system of claim 21 , wherein said one or more processors are further configured to determine the reputation score of a data source based on at least one or more event reports in the data source, a veracity associated with each of the one or more event reports, a second time stamp associated with each of the one or more event reports and a criticality score associated with each of the one or more events based on at least historical data associated with each of the one or more events.

23. The system of claim 22 , wherein the veracity associated with each of the one or more event reports is based on the first information and the second information.

24. The system of claim 16 , wherein the first information is determined based on one or more preferences of a requestor for each of the one or more events and the determined second information corresponds to a data source reliability score that is determined based on a reporting time stamp associated with each of the one or more event reports, a criticality score associated with each of the reported one or more events, and a pre-determined category of the one or more data sources and/or the likelihood of occurrence of one or more other events due to the occurrence of each of the one or more events.

25. The system of claim 16 , wherein the metadata associated with each of the one or more events comprises of an occurrence time stamp associated with each of the one or more event reports, a geographical location associated with each of the one or more event reports, and a criticality score associated with each of the one or more events.

26. The system of claim 16 , further comprising displaying, by the one or more processors, the reputation score and a likelihood of occurrence of one or more other events as a result of the occurrence of each of the one or more events on the display screen of the computing device of a requestor.

27. A computer program product for use with a computer, the computer program product comprising a non-transitory computer readable medium, wherein the non-transitory computer readable medium stores a computer program code for managing crowd-sensed data, associated with events occurring in a geographical area, at a computing device, wherein the computer program code is executable by one or more processors to: receive the crowd-sensed data from one or more data sources, wherein the crowd-sensed data comprises one or more event reports associated with at least a type of each of one or more events reported by the one or more data sources and a particular geographical area associated with the one or more events, and wherein the crowd-sensed data is received in real-time; generate a data structure based on an aggregation of the received crowd-sensed data; determine first information and second information based on at least the generated data structure, a reputation score associated with each of the one or more data sources and metadata associated with each of the one or more event reports, wherein the first information corresponds to a likelihood of an occurrence of one or more other events at the particular geographical area, and wherein the first information is determined using: Z ⁡ ( j ) = A ⁡ ( j ) × d j ′ A ⁡ ( j ) × d j ′ + B ⁡ ( j ) × ( 1 - d j ) ′ ; and display at least the determined first information and the determined second information through a user interface displayed on a display screen of a requestor-computing device based on at least a prioritization of the one or more events, wherein the one or more other events occur as a result of the occurrence of the one or more events, wherein the one or more other events correspond to traffic-related events, and wherein the traffic-related events include one or more of traffic jams, vehicle detours, and road accidents.